Gastos Casa
25 julio, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::scroll_box(width = "100%", height = "300px")| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 7/6/2022 | Comida | 23.450 | Andrés | NA |
| 13/6/2022 | Comida | 57.775 | Tami | NA |
| 18/6/2022 | Gas | 81.350 | Andrés | NA |
| 19/6/2022 | VTR | 21.990 | Andrés | NA |
| 20/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 21/6/2022 | Comida | 38.000 | Andrés | NA |
| 21/6/2022 | Comida | 15.000 | Andrés | Flor de loto verduras |
| 24/6/2022 | Comida | 40.400 | Andrés | Bar la Providencia |
| 27/6/2022 | Agua | 12.502 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 29/6/2022 | Netflix | 8.320 | Tami | NA |
| 29/6/2022 | Comida | 68.213 | Tami | NA |
| 30/6/2022 | Comida | 15.310 | Tami | NA |
| 30/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 2/7/2022 | Diosi | 35.990 | Andrés | NA |
| 3/7/2022 | Gas | 19.600 | Andrés | NA |
| 3/7/2022 | Parafina | 44.029 | Tami | NA |
| 11/7/2022 | Diosi | 15.930 | Tami | NA |
| 11/7/2022 | Comida | 60.660 | Tami | NA |
| 14/7/2022 | Enceres | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Comida | 15.000 | Andrés | NA |
| 19/7/2022 | Parafina | 22.521 | Tami | NA |
| 20/7/2022 | VTR | 21.990 | Andrés | NA |
| 21/7/2022 | Comida | 24.660 | Andrés | NA |
| 23/7/2022 | Enceres | 14.315 | Andrés | NA |
| 23/7/2022 | Comida | 22.263 | Andrés | NA |
| 20/7/2022 | Comida | 41.830 | Andrés | NA |
| 31/3/2019 | Comida | 9.000 | Andrés | NA |
| 8/9/2019 | Comida | 24.588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.1420e+08 2 4.6024 0.0105 *
## lag_depvar 7.3976e+10 1 1643.9759 <2e-16 ***
## Residuals 2.1014e+10 467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 863.433 13594.24 0.0213702
## 2-0 27969.635 22076.415 33862.85 0.0000000
## 2-1 20740.797 17147.407 24334.19 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 33 31622.57 0 27223.29
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## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 315 50203.90 16312.166
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2072.363134 4063.287363 -560.783693 2418.382449 -3014.543070
## 7 8 9 10 11
## 502.422025 -5679.849503 -1160.598978 -3936.090755 -359.346216
## 12 13 14 15 16
## -4889.445802 -1523.956565 -814.833103 455.276625 -3183.279373
## 17 18 19 20 21
## -300.219562 -2063.518385 6677.475182 -1532.173260 -1201.442768
## 22 23 24 25 26
## 1488.045686 -1194.517432 233.979175 1687.328335 -7129.550489
## 27 28 29 30 31
## 985.470883 8211.867437 353.529366 -79.194231 -2462.191500
## 32 33 34 35 36
## 1540.146819 4521.424966 1034.577977 2295.427087 -1979.108470
## 37 38 39 40 41
## 4523.638646 4254.044049 -2359.657724 -3033.792680 -1128.270222
## 42 43 44 45 46
## -10747.252971 7384.800886 2571.742469 1355.066977 8081.627622
## 47 48 49 50 51
## 589.039050 6437.066871 6572.547702 -6069.748693 -4904.309678
## 52 53 54 55 56
## -5110.178319 -7925.483956 6206.626917 -4067.494659 -4848.456352
## 57 58 59 60 61
## 3941.471819 926.030749 -7.501051 163.619813 -4979.487512
## 62 63 64 65 66
## 18188.134799 3523.525838 -3783.048396 5839.624753 7213.123447
## 67 68 69 70 71
## 14455.190432 1394.877561 -13489.560480 -1424.078952 4552.462993
## 72 73 74 75 76
## -5023.798047 -4466.666028 -10510.567938 2554.046611 -5346.967638
## 77 78 79 80 81
## 1160.547495 -6791.958232 676.313254 -2246.917614 -2577.855779
## 82 83 84 85 86
## -3805.361097 -390.141997 2448.189679 3857.224796 523.605962
## 87 88 89 90 91
## -448.680060 232.083998 4330.646735 -1178.937825 1147.492226
## 92 93 94 95 96
## -2078.669168 -1037.621884 192.870980 286.069410 -7477.166938
## 97 98 99 100 101
## 2468.100346 -8558.687133 -2821.231328 -3907.525260 -1583.360975
## 102 103 104 105 106
## -1111.016064 3324.080477 -2247.343143 2698.606353 -1091.888326
## 107 108 109 110 111
## 1039.186410 2637.609598 -3135.085049 -4676.719961 -765.438726
## 112 113 114 115 116
## 1985.368371 11746.727646 -1307.065907 2623.571027 4197.986709
## 117 118 119 120 121
## 3405.393026 -1218.364407 -4809.709090 -3761.394032 2322.102205
## 122 123 124 125 126
## -1752.837135 1338.869220 8843.914023 750.536403 37.755554
## 127 128 129 130 131
## -2603.801607 2606.473014 6984.652887 886.161478 -8619.636814
## 132 133 134 135 136
## 1724.142158 4096.739381 -3237.271119 -1454.233192 -871.041386
## 137 138 139 140 141
## -3887.177476 1213.137367 -480.697738 -2896.341424 1760.458948
## 142 143 144 145 146
## -1860.714002 -7794.070754 2143.927233 -3408.077958 2197.341671
## 147 148 149 150 151
## -194.658605 1079.933224 -319.672414 1389.802820 1206.290780
## 152 153 154 155 156
## 3362.119675 -4889.097281 -1152.740497 -3206.094565 6012.908787
## 157 158 159 160 161
## 9739.012570 -3117.263276 -4445.407344 3965.734350 502.205596
## 162 163 164 165 166
## 2988.300937 -5655.775766 -6443.414586 4511.875754 17688.036703
## 167 168 169 170 171
## 3737.340878 -314.749211 -2344.971757 -967.893256 3744.328154
## 172 173 174 175 176
## -105.362476 -7942.065725 3083.440666 4512.910468 770.209573
## 177 178 179 180 181
## 8894.188886 -9185.986513 -3302.239310 -10538.722020 -10930.603145
## 182 183 184 185 186
## 1639.876392 9658.426704 -1179.796805 6184.506260 6742.162722
## 187 188 189 190 191
## 13277.051256 8421.922090 -4137.374485 2459.283873 10357.276753
## 192 193 194 195 196
## -1740.720363 -2494.367209 -10280.401120 -6238.829162 1427.658139
## 197 198 199 200 201
## -5055.288021 -9565.715729 5706.884785 -2818.241840 -1440.413827
## 202 203 204 205 206
## -526.795064 6766.843827 10073.208586 661.427930 3010.357877
## 207 208 209 210 211
## 3161.856693 5827.561460 12830.919515 -5803.568475 -11318.887284
## 212 213 214 215 216
## -5548.415856 -10404.166722 -4783.250701 1857.043954 -12715.282637
## 217 218 219 220 221
## 16805.182495 8023.766017 1654.240511 26802.482613 12364.368242
## 222 223 224 225 226
## 7079.410149 13743.973463 -4290.252508 -2010.978501 3578.558445
## 227 228 229 230 231
## 166.403327 2593.714287 8864.087179 5636.272078 -2112.981641
## 232 233 234 235 236
## -1966.611749 9345.372844 -11655.517932 -7271.298530 -8433.804667
## 237 238 239 240 241
## -9897.642021 3382.350893 1608.260189 -8065.482645 -8678.714385
## 242 243 244 245 246
## 9482.354575 -7499.810636 2822.359653 -10012.684109 -3672.232533
## 247 248 249 250 251
## 1820.536762 1357.304520 -11995.604216 4074.014076 2421.702601
## 252 253 254 255 256
## 4524.713920 2384.071648 -945.231912 11359.529070 20973.832118
## 257 258 259 260 261
## 3070.965582 -4399.464661 4060.305804 -1764.094915 3712.433778
## 262 263 264 265 266
## -4895.450974 -10861.513369 -4564.822085 -308.429129 -4976.872966
## 267 268 269 270 271
## 9037.841123 -4130.170687 4384.917279 -1963.782084 4596.522081
## 272 273 274 275 276
## 822.930155 7411.917600 -1380.358456 12084.487427 -4650.130373
## 277 278 279 280 281
## 1732.852602 -369.364991 7873.866377 -5109.528605 -2703.940966
## 282 283 284 285 286
## -11189.423150 -2461.196004 18885.977572 7793.133414 2670.446265
## 287 288 289 290 291
## -699.692217 866.630211 6369.382422 6800.203815 -18907.099286
## 292 293 294 295 296
## -11023.386522 -7871.409565 9999.097517 3274.278522 -1017.777487
## 297 298 299 300 301
## 27576.241302 9917.493247 4670.451455 9275.390013 2549.645119
## 302 303 304 305 306
## -1317.950785 7675.475187 -24563.797123 -3464.262857 -50.490210
## 307 308 309 310 311
## -6835.284506 -3751.010768 3195.350843 -8973.676354 -2906.128477
## 312 313 314 315 316
## -7839.619687 1991.451556 -2772.217150 2441.521740 -3739.733393
## 317 318 319 320 321
## 27816.623655 -727.049107 3314.305104 10828.421379 5474.420985
## 322 323 324 325 326
## 32230.317442 4608.792207 -21418.871579 1642.966843 978.027406
## 327 328 329 330 331
## -6574.211944 -1730.915879 -33222.354652 1384.014376 -1841.326981
## 332 333 334 335 336
## 369.982370 -2730.269845 4537.925662 -61.805326 -6589.633950
## 337 338 339 340 341
## -2683.743994 -1744.442727 -7231.086560 4369.200030 -936.956179
## 342 343 344 345 346
## -1311.870368 -570.617117 588.282259 868.238732 -1258.270731
## 347 348 349 350 351
## -9083.744539 -12747.477094 2907.723370 -3800.158341 -3117.801853
## 352 353 354 355 356
## -5431.872939 2334.302356 1901.160821 3214.884747 -3371.453934
## 357 358 359 360 361
## -97.619714 1076.091407 7379.590203 532.466374 206.618642
## 362 363 364 365 366
## 2822.426518 -2548.965717 -640.282852 -8498.085540 -4270.558223
## 367 368 369 370 371
## -5812.611996 -4489.953760 -6756.812086 5572.062057 821.167064
## 372 373 374 375 376
## 7535.797721 -7338.371898 -1875.865848 -2991.318310 -2047.223086
## 377 378 379 380 381
## -12029.091971 2468.453404 -10134.907003 6299.795860 9817.952663
## 382 383 384 385 386
## 3451.591845 -2130.060044 1895.762407 7002.505683 11571.775707
## 387 388 389 390 391
## -5790.572529 -5258.967800 23.754903 8745.926733 1883.432510
## 392 393 394 395 396
## 11277.806240 -9958.695802 2850.507061 762.623209 616.549539
## 397 398 399 400 401
## -593.714042 -481.499359 -14387.577643 8829.670326 -995.779353
## 402 403 404 405 406
## -1168.508457 7204.764250 -7804.892949 -1052.964980 -2272.024416
## 407 408 409 410 411
## -5528.652130 -2496.358446 -3530.483146 -8333.662107 6650.490220
## 412 413 414 415 416
## 2040.064752 -7017.849419 -7253.716255 14737.431369 4101.645505
## 417 418 419 420 421
## 4712.191534 -7881.971814 -4476.278748 -2274.360274 3170.120722
## 422 423 424 425 426
## -13713.855537 -2314.000022 -8612.618625 3591.674094 7472.865933
## 427 428 429 430 431
## 6942.515426 -3729.247033 -3814.383267 -4370.421908 -1389.353883
## 432 433 434 435 436
## -5308.439721 -6167.731726 -5429.529209 -828.470250 -304.977114
## 437 438 439 440 441
## -4459.246113 3129.081716 5312.703972 -4679.817729 -1732.562237
## 442 443 444 445 446
## 2006.431443 -3453.490101 3251.137023 -6224.113982 -11684.197911
## 447 448 449 450 451
## -3947.402641 10229.373240 -1618.687305 5172.751883 -5537.136695
## 452 453 454 455 456
## -724.346564 777.240749 3395.242479 -11956.265267 3833.763415
## 457 458 459 460 461
## -6309.008640 6983.758359 3360.042409 2800.765183 -3593.008701
## 462 463 464 465 466
## 2393.623392 257.458701 2053.678471 -287.980309 3590.019198
## 467 468 469 470 471
## -2445.131561 6038.947433 -6784.554819 -2704.336388 -1907.197292
## 472
## -4342.009763
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17196.92 20075.71 24376.93 24091.76 26471.26 23774.29 24498.56 19677.74
## 10 11 12 13 14 15 16 17
## 19411.38 16724.63 17510.73 14203.81 14255.55 14927.58 16642.99 14944.36
## 18 19 20 21 22 23 24 25
## 15990.52 15357.10 22518.17 21592.01 21066.10 22977.09 22295.59 22955.39
## 26 27 28 29 30 31 32 33
## 24821.84 18682.81 20428.13 28352.47 28410.77 28080.05 25683.14 27101.15
## 34 35 36 37 38 39 40 41
## 30986.85 31339.14 32763.97 30246.93 34188.96 37432.66 34456.08 31231.56
## 42 43 44 45 46 47 48 49
## 30066.54 20541.48 28143.69 30607.22 31708.52 38622.53 38111.50 42825.45
## 50 51 52 53 54 55 56 57
## 47108.75 39725.60 34233.75 29201.20 22269.52 28629.35 25172.03 21428.53
## 58 59 60 61 62 63 64 65
## 25885.83 27159.36 27459.67 27876.06 23701.15 40476.62 42341.05 37534.23
## 66 67 68 69 70 71 72 73
## 41787.88 46758.10 57544.69 55536.42 40615.79 38093.97 41145.37 35382.24
## 74 75 76 77 78 79 80 81
## 30784.00 21384.24 24621.25 20501.74 22610.96 17449.83 19487.63 18705.57
## 82 83 84 85 86 87 88 89
## 17722.50 15770.00 17061.95 20710.06 25176.82 26177.68 26202.92 26826.50
## 90 91 92 93 94 95 96 97
## 30997.37 29814.94 30825.38 28868.34 28059.27 28431.50 28842.60 22348.76
## 98 99 100 101 102 103 104 105
## 25397.26 18350.37 17193.81 15212.79 15515.87 16200.78 20723.06 19796.39
## 106 107 108 109 110 111 112 113
## 23346.46 23134.10 24828.82 27737.51 25207.86 21611.87 21890.35 24565.99
## 114 115 116 117 118 119 120 121
## 35551.07 33723.86 35581.73 38613.32 40590.94 38253.71 33017.25 29318.04
## 122 123 124 125 126 127 128 129
## 31423.98 29684.85 30879.51 38563.61 38202.10 37253.23 34081.96 35882.92
## 130 131 132 133 134 135 136 137
## 41340.70 40774.78 31878.86 33157.69 36382.84 32753.66 31123.04 30197.89
## 138 139 140 141 142 143 144 145
## 26716.72 28146.84 27913.91 25574.54 27621.43 26230.93 19762.07 22826.22
## 146 147 148 149 150 151 152 153
## 20628.80 23638.94 24184.92 25792.96 25977.05 27649.57 28964.74 32030.53
## 154 155 156 157 158 159 160 161
## 27450.45 26705.24 24233.38 30192.84 41137.69 39449.41 36785.12 41861.08
## 162 163 164 165 166 167 168 169
## 43285.27 46739.06 42154.70 37409.84 42895.25 59378.23 61614.89 60011.40
## 170 171 172 173 174 175 176 177
## 56801.89 55183.39 57915.93 56929.21 49135.85 51990.66 55774.79 55811.38
## 178 179 180 181 182 183 184 185
## 63019.27 53416.24 50131.15 40837.89 32283.41 35830.57 46046.08 45496.07
## 186 187 188 189 190 191 192 193
## 51514.84 57323.52 68226.08 73567.52 67192.29 67387.87 74536.58 70165.08
## 194 195 196 197 198 199 200 201
## 65638.26 54762.83 48726.77 50166.86 45712.72 37794.69 44290.67 42498.41
## 202 203 204 205 206 207 208 209
## 42132.37 42616.01 49485.36 58473.14 58098.64 59842.57 61516.72 65349.94
## 210 211 212 213 214 215 216 217
## 74921.43 66916.46 54974.56 49523.60 40420.11 37344.10 40492.28 30401.82
## 218 219 220 221 222 223 224 225
## 47563.52 54965.47 55877.37 78895.20 86473.30 88498.74 96174.25 87024.84
## 226 227 228 229 230 231 232 233
## 80956.73 80534.03 77146.86 76299.06 81088.59 82467.98 76841.75 72001.63
## 234 235 236 237 238 239 240 241
## 77717.95 64217.73 56165.95 48027.36 39545.93 43784.31 45960.91 39339.00
## 242 243 244 245 246 247 248 249
## 32948.50 43344.95 37528.07 41507.40 33685.52 32377.03 36072.84 38928.03
## 250 251 252 253 254 255 256 257
## 29655.84 35659.73 39503.29 44755.64 47504.09 46991.04 57406.17 75097.32
## 258 259 260 261 262 263 264 265
## 74910.32 68146.84 69645.09 65823.99 67286.17 60974.66 50130.39 46113.71
## 266 267 268 269 270 271 272 273
## 46325.44 42389.02 51290.74 47522.51 51715.21 49810.91 53923.36 54222.65
## 274 275 276 277 278 279 280 281
## 60306.79 57914.80 67694.99 61552.43 61764.79 60095.56 65902.10 59563.08
## 282 283 284 285 286 287 288 289
## 56088.85 45525.34 43904.31 61327.58 66918.98 67332.98 64721.94 63799.19
## 290 291 292 293 294 295 296 297
## 67844.51 71798.10 52583.96 42576.27 36520.90 46956.72 50234.49 49338.62
## 298 299 300 301 302 303 304 305
## 73803.22 79814.55 80489.61 85153.21 83331.81 78306.95 81812.23 56432.69
## 306 307 308 309 310 311 312 313
## 52652.35 52328.57 46049.87 43228.36 46871.68 39341.27 38049.19 32550.41
## 314 315 316 317 318 319 320 321
## 36376.93 35549.19 39423.16 37385.23 63457.62 61274.84 62916.44 71003.29
## 322 323 324 325 326 327 328 329
## 73417.11 99181.49 97541.16 73103.18 71887.69 70226.78 62089.20 59179.50
## 330 331 332 333 334 335 336 337
## 28794.41 32522.90 32967.30 35312.98 34646.50 40477.52 41565.06 36759.89
## 338 339 340 341 342 343 344 345
## 35965.59 36093.66 31360.66 37426.24 38097.01 38358.33 39243.86 41049.62
## 346 347 348 349 350 351 352 353
## 42891.84 42640.74 35507.05 25970.13 31374.16 30222.52 29808.02 27397.98
## 354 355 356 357 358 359 360 361
## 32128.84 35924.83 40438.03 38606.91 39881.19 42043.41 49520.82 50077.52
## 362 363 364 365 366 367 368 369
## 50281.43 52771.97 50227.43 49665.80 42229.27 39394.90 35529.38 33283.38
## 370 371 372 373 374 375 376 377
## 29297.37 36666.26 38978.63 46951.80 40856.44 40297.46 38818.51 38346.09
## 378 379 380 381 382 383 384 385
## 29112.26 33761.48 26735.92 35046.62 45494.55 49099.63 47353.81 49367.64
## 386 387 388 389 390 391 392 393
## 55656.94 65247.86 58383.68 52790.39 52516.07 59977.71 60506.91 69271.98
## 394 395 396 397 398 399 400 401
## 58256.49 59840.81 59396.02 58874.14 57344.21 56092.01 42703.33 51384.49
## 402 403 404 405 406 407 408 409
## 50373.79 49328.52 55801.04 48260.54 47564.02 45872.08 41501.22 40318.91
## 410 411 412 413 414 415 416 417
## 38361.23 32389.65 40350.08 43308.99 37922.00 32955.57 47992.78 51880.38
## 418 419 420 421 422 423 424 425
## 55853.40 48238.71 44521.07 43182.31 46808.71 35098.86 34825.05 29019.90
## 426 427 428 429 430 431 432 433
## 34671.99 43092.34 50061.25 46790.67 43826.71 40717.64 40604.58 37043.16
## 434 435 436 437 438 439 440 441
## 33138.53 30341.76 31935.41 33805.39 31787.78 36708.15 42982.82 39698.99
## 442 443 444 445 446 447 448 449
## 39401.71 42441.63 40304.15 44338.11 39532.06 30464.40 29288.91 40772.40
## 450 451 452 453 454 455 456 457
## 40450.39 46164.57 41752.06 42105.62 43744.19 47503.84 37265.24 42168.58
## 458 459 460 461 462 463 464 465
## 37540.81 45194.24 48753.52 51403.29 48096.38 50463.26 50667.04 52433.55
## 466 467 468 469 470 471 472
## 51925.55 54902.13 52200.62 57308.13 50492.91 48077.20 46647.58
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8579
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.602357 0.5727316 2.932086
## t2* 1643.975933 30.1672587 250.781792
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.278922 4.705285 10.56854
## 2 lag_depvar 1289.983774 1657.752186 2116.40933Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jul 25 00:49:19 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jul 25 00:49:26 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jul 25 00:49:32 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jul 25 00:49:39 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jul 25 00:49:46 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jul 25 00:49:53 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jul 25 00:50:00 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jul 25 00:50:06 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jul 25 00:50:13 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jul 25 00:50:20 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3) %>%
knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
col.names= c("Item","2023","2022","2021","2020")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 7.328667 | 6.342333 | 7.780500 |
| Comida | NA | 304.551500 | 313.448222 | 345.242400 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 37.112000 | 32.052667 | 27.473433 |
| Enceres | NA | 10.915000 | 13.505778 | 23.708267 |
| Farmacia | NA | 3.663333 | 10.551833 | 11.945800 |
| Gas/Bencina | NA | 54.006667 | 27.058000 | 23.138133 |
| Diosi | NA | 13.517833 | 39.631167 | 38.627233 |
| donaciones/regalos | NA | 0.000000 | 9.560111 | 9.157300 |
| Electrodomésticos/ Mantención casa | NA | 7.888000 | 40.359333 | 27.648933 |
| VTR | NA | 28.990000 | 22.387944 | 21.078267 |
| Netflix | NA | 7.369500 | 7.142333 | 7.584300 |
| Otros | NA | 6.302167 | 2.100722 | 1.260433 |
| Total | 0 | 481.644667 | 524.140444 | 544.645000 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1682, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-08-09 00:04:58 sería de: 34.530 pesos// Percentil 95% más alto proyectado: 37.582,14
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 33599.83 | 33568.79 |
| Lo.80 | 33689.29 | 33661.54 |
| Point.Forecast | 34529.69 | 36388.94 |
| Hi.80 | 36218.05 | 41084.90 |
| Hi.95 | 37144.98 | 43570.79 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3280 985.7562
## s.e. 0.1535 38.4750
##
## sigma^2 = 29191: log likelihood = -267.98
## AIC=541.96 AICc=542.61 BIC=547.1paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3618 33.3818
## s.e. 0.1548 1.3979
##
## sigma^2 = 30136: log likelihood = -268.65
## AIC=543.3 AICc=543.94 BIC=548.44forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 875.1395 | 631.2725 | 664.1008 |
| Lo.80 | 1001.4686 | 753.9717 | 743.6058 |
| Point.Forecast | 1240.1098 | 985.7558 | 920.6852 |
| Hi.80 | 1478.7510 | 1217.5400 | 1219.2415 |
| Hi.95 | 1605.0801 | 1340.2391 | 1414.6897 |
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.0
## [7] ggiraph_0.8.2 tidytext_0.3.3 DT_0.23
## [10] autoplotly_0.1.4 rvest_1.0.2 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.16 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.1 forcats_0.5.1
## [22] dplyr_1.0.9 purrr_0.3.4 tidyr_1.2.0
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.10 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.7 httr_1.4.3
## [34] readxl_1.4.0 zoo_1.8-10 stringr_1.4.0
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_3042.79 fBasics_3042.89.2
## [43] timeSeries_4021.104 timeDate_4021.104 plyr_1.8.7
## [46] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.39 uuid_1.1-0 rstudioapi_0.13
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.7.5
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.4.1 rprojroot_2.0.3 vctrs_0.4.1
## [25] generics_0.1.3 xfun_0.31 R6_2.5.1
## [28] bitops_1.0-7 cachem_1.0.6 assertthat_0.2.1
## [31] networkD3_0.4 vroom_1.5.7 nnet_7.3-16
## [34] googlesheets4_1.0.0 gtable_0.3.0 spatial_7.3-14
## [37] rlang_1.0.4 forge_0.2.0 systemfonts_1.0.4
## [40] splines_4.1.2 lazyeval_0.2.2 gargle_1.2.0
## [43] selectr_0.4-2 broom_1.0.0 yaml_2.3.5
## [46] abind_1.4-5 modelr_0.1.8 crosstalk_1.2.0
## [49] backports_1.4.1 quantmod_0.4.20 tokenizers_0.2.1
## [52] tools_4.1.2 ellipsis_0.3.2 gplots_3.1.3
## [55] kableExtra_1.3.4 jquerylib_0.1.4 Rcpp_1.0.9
## [58] base64enc_0.1-3 fracdiff_1.5-1 haven_2.5.0
## [61] fs_1.5.2 magrittr_2.0.3 lmtest_0.9-40
## [64] reprex_2.0.1 googledrive_2.0.0 mvtnorm_1.1-3
## [67] sjmisc_2.8.9 hms_1.1.1 evaluate_0.15
## [70] xtable_1.8-4 sjstats_0.18.1 ggeffects_1.1.2
## [73] compiler_4.1.2 KernSmooth_2.23-20 crayon_1.5.1
## [76] minqa_1.2.4 htmltools_0.5.3 tzdb_0.3.0
## [79] lubridate_1.8.0 DBI_1.1.3 sjlabelled_1.2.0
## [82] dbplyr_2.2.1 boot_1.3-28 Matrix_1.3-4
## [85] car_3.1-0 cli_3.3.0 quadprog_1.5-8
## [88] parallel_4.1.2 insight_0.18.0 igraph_1.3.4
## [91] pkgconfig_2.0.3 xml2_1.3.3 svglite_2.1.0
## [94] bslib_0.4.0 webshot_0.5.3 estimability_1.4
## [97] anytime_0.3.9 snakecase_0.11.0 janeaustenr_0.1.5
## [100] digest_0.6.29 parameters_0.18.1 janitor_2.1.0
## [103] rmarkdown_2.14 cellranger_1.1.0 curl_4.3.2
## [106] gtools_3.9.3 urca_1.3-0 nloptr_2.0.3
## [109] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
## [112] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.0
## [115] fansi_1.0.3 pillar_1.8.0 fastmap_1.1.0
## [118] glue_1.6.2 bayestestR_0.12.1 bit_4.0.4
## [121] sass_0.4.2 performance_0.9.1 r2d3_0.2.6
## [124] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))